Apache Spark Performance Tuning and Best Practices

Learn essential best practices for running Apache Spark in production environments and optimize system-level performance in this 42-minute tutorial. Explore major bottlenecks, understand Spark casting techniques, and discover the advantages of broadcast operations. Dive into serialization, DataFrame operations, and UDF implementation. Master data filtering, supply reduction, and file format optimization. Gain insights on executor optimization, memory tuning, and handling out-of-memory errors. Equip yourself with the knowledge to fine-tune Apache Spark for peak performance in real-world scenarios.

Intro
What is Spark
How to optimize
Major bottlenecks
Spark Casting
Spark Casting Demo
Disadvantages of Casting
Advantages of Broadcast
Architecture of Broadcast
Serialization
Serializer
DataFrame
UDF
Filter Data
Supply
Reducing Supply
Importance of File Format
Handling of Data
File Format Optimization
Executor Optimization
Out of Memory
Memory Tuning
Conclusion